Identification method and apparatus and computer-readable storage medium

ABSTRACT

An identification method includes: controlling at least one camera to acquire a face image and an eye image of a target object, wherein the eye image includes at least one of an iris feature or an eye-print feature; and identifying the target object based on the face image and the eye image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims priority to Chinese PatentApplication No. 201810917915.7, filed on Aug. 13, 2018, the entirecontent of all of which is incorporated herein by reference.

TECHNICAL FIELD

The specification relates generally to the field of computertechnologies and, more particularly, to an identification method, andapparatus and computer-readable storage medium.

TECHNICAL BACKGROUND

With the development of communication technologies and thepopularization of electronic payment, people conduct transactionsincreasingly by electronic payment (such as payment by Alipay™) insteadof cash.

Currently, it is common to use terminals such as mobile phones tocomplete electronic payment. However, terminals such as mobile phonesgenerally use a payment password for identification or verificationduring electronic payment. Once a user's mobile phone and paymentpassword are stolen at the same time, the user will suffer economiclosses. Therefore, how to better identify a user when the user makes atransaction by electronic payment is a technical problem to be solved.

SUMMARY

An identification method, apparatus and computer-readable storage mediumare provided in the embodiments of this specification to better identifya user.

According to a first aspect, an identification method includes:controlling at least one camera to acquire a face image and an eye imageof a target object, wherein the eye image includes at least one of aniris feature or an eye-print feature; and identifying the target objectbased on the face image and the eye image.

According to a second aspect, an identification apparatus includes: anacquisition module configured to acquire a face image and an eye imageof a target object through at least one camera, wherein the eye imageincludes at least one of an iris feature or an eye-print feature; and anidentification module configured to identify the target object based onthe face image and the eye image.

According to a third aspect, an identification device includes: aprocessor; and a memory configured to store instructions executable bythe processor, wherein the processor is configured to: control at leastone camera to acquire a face image and an eye image of a target object,wherein the eye image includes at least one of an iris feature or aneye-print feature; and identify the target object based on the faceimage and the eye image.

According to a fourth aspect, a computer-readable storage medium hasstored thereon instructions that, when executed by a processor of adevice, cause the device to perform an identification method, theidentification method including: controlling at least one camera toacquire a face image and an eye image of a target object, wherein theeye image includes at least one of an iris feature or an eye-printfeature; and identifying the target object based on the face image andthe eye image.

At least one of the above technical solutions used in the embodiments ofthis specification can achieve the following beneficial effects.

In the embodiments of this specification, a face image and an eye imageof a target object are acquired, and the target object is identifiedbased on at least one of an iris feature or an eye-print feature in theeye image. Because the target object is verified using multiple factorsby combining the face image with the at least one of the iris feature orthe eye-print feature in the eye image, a user can be better identified.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of an identification method according to anembodiment.

FIG. 2 is a flowchart of an identification method according to anotherembodiment.

FIG. 3 is a block diagram of an identification apparatus according to anembodiment.

FIG. 4 is a schematic diagram of a terminal according to an embodiment.

DETAILED DESCRIPTION

Embodiments of this specification are described below with reference tothe accompanying drawings. The described embodiments are merelyexamples, rather than all of the embodiments consistent with thespecification. Based on the described embodiments, all other embodimentsobtained by those with ordinary skill in the art without creativeefforts belong to the protection scope of the specification.

In the embodiments, facial recognition is a biometric recognitiontechnology for identification based on human facial feature information,also commonly referred to as portrait recognition, face recognition.

In the embodiments, iris is a circular part between the black pupil andthe white sclera in the eye, including detail features such as manyintersecting spots, thin threads, coronas, stripes, and crypts. The irisremains unchanged over one's entire life course after fetal development.These features determine the uniqueness of the iris feature, and alsodetermine the uniqueness of identification. Therefore, the iris featureof the eye can be used as an identifying object of each person.

In the embodiments, eye-print refers to blood vessels, stripes, andother subtle features in the white part of the eye (sclera). Studiesshow that these detailed features remain unchanged over a long time, andthe eye-print feature is unique to each person. Even identical multipleshave different eye-prints.

In the embodiments, liveness detection refers to detecting whether abiometric signal obtained by a sensor is from a real living human body.For human face liveness detection, liveness detection technologies aremainly used to ensure that a human face image acquired by a camera comesfrom the face of a living body, rather than a photo, video, human facemask, or the like.

The technical solutions according to the embodiments of thespecification are described in detail below with reference to theaccompanying drawings.

The identification method according to the embodiments of thisspecification can be widely applied in various scenarios, such aspayment scenarios, medical scenarios, passenger scenarios, and the like.

FIG. 1 is a flowchart of an identification method 100 according to anembodiment. Referring to FIG. 1, the identification method 100 mayinclude the following steps.

In step 110, at least one camera is controlled to acquire a face imageand an eye image of a target object, wherein the eye image includes atleast one of an iris feature or an eye-print feature.

The target object is an object to be identified, and it may be a humanbeing, an animal, or the like. For example, when user A needs to beidentified, user A may stand in front of a machine to wait for a camerato acquire a face image and an eye image of user A. In this case, user Ais the target object.

In step 120, the target object is identified based on the face image andthe eye image.

In one embodiment, the target object may be identified based on the faceimage and the iris feature in the eye image. In another embodiment, thetarget object may be identified based on the face image and theeye-print feature in the eye image.

In another embodiment, the target object may be identified based on theface image as well as the iris feature and the eye-print feature in theeye image.

In the embodiments of this specification, a face image and an eye imageof a target object are acquired, and the target object is identifiedbased on at least one of an iris feature or an eye-print feature in theeye image. Because the target object is verified using multiple factorsby combining the face image with at least one of the iris feature or theeye-print feature in the eye image, a user can be better identified.

In the embodiments of this specification, the face image and the eyeimage may be acquired by one camera or by two cameras. In the case ofone camera, the face image and the eye image of the target object may beacquired by the same camera. In the case of two cameras, the face imageof the target object may be acquired by one camera, and the eye image ofthe target object may be acquired by the other camera.

In the embodiments of this specification, at least one camera can becontrolled in various manners to acquire the face image and the eyeimage of the target object. The manners are described in the followingwith examples.

In some embodiments, the face image and the eye image are acquired byusing rectangular frames. A facial rectangular frame may be used duringacquisition of the face image, and a two-eye rectangular frame may beused during acquisition of the eye image. The facial rectangular frameis a rectangular frame outlining the face of the target object, and thetwo-eye rectangular frame includes rectangular frames outlining the twoeyes of the target object, that is, a left-eye rectangular frame and aright-eye rectangular frame. Rectangular frames can be used foralignment with various parts of a body. Therefore, a clear face imageand a clear eye image can be obtained when rectangular frames are usedfor image acquisition, thereby facilitating subsequent identification.

In one embodiment, the face image and the eye image may be acquiredseparately. For example, the face image of the target object is acquiredfirst, and then the eye image of the target object is acquired. In thisembodiment, the step of acquiring a face image and an eye image of atarget object may include: adjusting the at least one camera to acquirethe face image of the target object; and adjusting the at least onecamera based on the position of a facial rectangular frame of the faceimage to acquire the eye image of the target object. In this manner, theface image and the eye image of the target object can be acquiredquickly by using a facial rectangular frame when the target object doesnot cooperate.

In the specification, adjusting the camera may be adjusting imagingparameters of the camera, such as a focal length, orientation, exposuretime, and the like. An objective of adjusting the camera is to obtain aface image or an eye image of higher resolution and clarity.

When there is one camera, the step of adjusting the at least one camerato acquire the face image of the target object may include: adjustingthe camera to obtain the position of the facial rectangular frame of thetarget object; and acquiring the face image of the target object basedon the position of the facial rectangular frame. Correspondingly, thestep of adjusting the at least one camera based on the position of afacial rectangular frame of the face image to acquire the eye image ofthe target object may include: adjusting the camera based on theposition of the facial rectangular frame of the face image to obtain theposition of a two-eye rectangular frame of the target object; andacquiring the eye image of the target object based on the position ofthe two-eye rectangular frame.

In other words, in the case of one camera, the camera can be adjusted toobtain the face image and the eye image of the target objectsequentially. Because the face image and the eye image are acquiredbased on the facial rectangular frame and the two-eye rectangular framein this process, the high quality of the acquired images can be ensured.At the same time, when the target object does not cooperate during imageacquisition, the face image and the eye image can also be obtainedquickly by adjusting the camera and using rectangular frames (includingthe facial rectangular frame and the two-eye rectangular frame).

When the number of cameras is two, one of the at least one camera may bea first camera for photographing a face, and the other camera may be asecond camera for photographing eyes. In this case, the step ofadjusting the at least one camera to acquire the face image of thetarget object may include: adjusting the first camera to obtain theposition of the facial rectangular frame of the target object; andacquiring the face image of the target object based on the position ofthe facial rectangular frame. Correspondingly, the step of adjusting theat least one camera based on the position of a facial rectangular frameof the face image to acquire the eye image of the target object mayinclude: adjusting the second camera based on the position of the facialrectangular frame to obtain the position of a two-eye rectangular frameof the target object; and acquiring the eye image of the target objectbased on the position of the two-eye rectangular frame.

In the case of two cameras, the face image and the eye image of thetarget object may be obtained by adjusting the two cameras. In anembodiment, the face image and the eye image are acquired by specialcameras. Therefore, images with higher quality than those in the case ofone camera can be obtained. Because the face image and the eye image areacquired based on the facial rectangular frame and the two-eyerectangular frame in the image acquisition process, the high quality ofthe acquired images can be ensured. At the same time, when the targetobject does not cooperate during image acquisition, the face image andthe eye image can also be obtained quickly by adjusting the cameras andusing rectangular frames (including the facial rectangular frame and thetwo-eye rectangular frame).

In another embodiment, the face image and the eye image can be acquiredsimultaneously. In this embodiment, the step of acquiring a face imageand an eye image of a target object may include: adjusting the at leastone camera to obtain the position of an initial facial rectangular frameof the target object; and adjusting the at least one camera based on theposition of the initial facial rectangular frame to acquire the faceimage and the eye image of the target object. In other words, in thiscase, the face image is not acquired when the initial facial rectangularframe appears; instead, the camera is further adjusted to ensure thatthe face image and the eye image are acquired simultaneously only whenthe eye image and the face image are both clear enough.

In the case of one camera, the step of adjusting the at least one camerabased on the position of the initial facial rectangular frame to acquirethe face image and the eye image of the target object may include:adjusting the camera based on the position of the initial facialrectangular frame to obtain the position of a target facial rectangularframe of the target object and the position of a two-eye rectangularframe of the target object; and acquiring the face image of the targetobject based on the position of the target facial rectangular frame, andacquiring the eye image of the target object based on the position ofthe two-eye rectangular frame.

In the case of one camera, by adjusting the camera continuously, theface image and the eye image are acquired only when the facialrectangular frame and the two-eye rectangular frame appear at the sametime. As such, it can be ensured that the acquired face image and eyeimage are associated at the same moment.

In the case of two cameras, the at least one camera includes a firstcamera for photographing a face and a second camera for photographingeyes, and the step of adjusting the at least one camera to obtain theposition of an initial facial rectangular frame of the target object mayinclude: adjusting the first camera to obtain the position of theinitial facial rectangular frame of the target object. Correspondingly,the step of adjusting the at least one camera based on the position ofthe initial facial rectangular frame to acquire the face image and theeye image of the target object may include: adjusting the first camerabased on the position of the initial facial rectangular frame to obtainthe position of a target facial rectangular frame of the target object,and acquiring the face image of the target object based on the positionof the target facial rectangular frame; adjusting the second camerabased on the position of the target facial rectangular frame to obtainthe position of a two-eye rectangular frame of the target object; andacquiring the eye image of the target object based on the position ofthe two-eye rectangular frame.

In the case of two cameras, the face image and the eye image of thetarget object can be obtained by adjusting the two cameras. In anembodiment, the face image and the eye image are acquired by specialcameras. Therefore, images with higher quality than those in the case ofone camera can be obtained. Because the face image and the eye image areacquired based on the facial rectangular frame and the two-eyerectangular frame in the image acquisition process, high quality of theacquired images can be ensured. At the same time, when the target objectdoes not cooperate during image acquisition, the face image and the eyeimage can also be obtained quickly by adjusting the cameras and usingrectangular frames (including the facial rectangular frame and thetwo-eye rectangular frame).

In some embodiments, the face image and the eye image are acquiredwithout using rectangular frames. In an embodiment, the camera may beadjusted continuously to ensure that the eye image and the face imageare clear enough. During camera adjustment, similar to the foregoingcases, the face image and the eye image may be acquired separately orsimultaneously. When the face image and the eye image are acquiredsimultaneously, the images are generally required to be of high quality.

It should be understood that the above descriptions are merely examplesof image acquisition methods, which are not intended to be limiting.

It should also be understood that in the embodiments of thisspecification, the face may be detected first during the process ofdetermining the facial rectangular frame. In some embodiments, the facedetection method may be a conventional method based on Haar features anda boost classifier, or a deep learning method based on a transientchaotic neural network (TCNN). The position of the facial rectangularframe may be the imaging coordinate position of the facial rectangularframe, and the position of the two-eye rectangular frame may be theimaging coordinate position of the two-eye rectangular frame.

FIG. 2 is a flowchart of an identification method 200 according to anembodiment. Referring to FIG. 2, the identification method 200 mayinclude the following steps.

In step 210, a face image of a target object is acquired, and at leastone of an iris feature or eye-print feature in an eye image of thetarget object is acquired.

In step 220, it is determined whether the target object is a living bodybased on the face image and the at least one of the iris feature or theeye-print feature in the eye image.

The face image may be a single-frame face image or a multi-frame faceimage. The eye image may be a single-frame eye image or a multi-frameeye image.

If the face image is a single-frame face image, and the eye image is asingle-frame eye image, step 220 of determining whether the targetobject is a living body based on the eye image and the face image mayinclude: determining whether the target object is a living body based ona difference between a single-frame face image of a living body and asingle-frame face image of a non-living body, and a difference betweenat least one of the iris feature or the eye-print feature included in asingle-frame eye image of a living body and the at least one of the irisfeature or the eye-print feature included in a single-frame eye image ofa non-living body.

In the case of a single-frame face image, the face of a non-living bodymay have distortions, flaws, or the like. Therefore, by determiningwhether the face displayed in the single-frame face image has anydistortions or flaws, it can be determined whether the target object isa living body. For example, if a single-frame face image has phenomenasuch as distortions and flaws, it can be directly determined that thetarget object is a non-living body (for example, a face in a video); ifa face displayed in a single-frame face image is intact and hasphenomena such as distortions and flaws, the target object can bedetermined to be a living body, or further detection may be performed incombination with at least one of the iris feature or the eye-printfeature.

In the case of a single-frame eye image, detail features on the iris(such as spots, thin threads, coronas, stripes, and crypts) or detailfeatures on the eye-print (such as blood vessels and stripes) may bedetected to determine whether the target object is a living body. Forexample, if the eye image includes detail features on the iris or detailfeatures on the eye-print, it can be determined that the target objectis a living body; if the eye image does not include detail features onthe iris or detail features on the eye-print, it can be determined thatthe target object is a non-living body.

In the embodiments of this specification, the face image and the eyeimage may be used in combination, and it is determined whether thetarget object is a living body by judging the face image and the eyeimage.

If the face image is a multi-frame face image, and the eye image is amulti-frame eye image, step 220 of determining whether the target objectis a living body based on the eye image and the face image includes:determining that the target object is a living body if there aredifferences among the multiple frames of the face image; determiningthat the target object is a living body if there are differences amongthe multiple frames of the eye image in terms of the iris feature or theeye-print feature, and determining that the target object is anon-living body if there is no difference between the multiple frames ofthe eye image in terms of the iris feature or the eye-print feature.

When the face image and the eye image have multiple frames, the multipleframes of the face image may be compared with each other to determinewhether the target object is a living body, and meanwhile, the multipleframes of the eye image are also compared with each other to determinewhether the target object is a living body. If there is no differencebetween the multiple frames of the eye image, it can be determined thatthe target object is a non-living body. Of course, to ensure stringency,the target object may be determined as a non-living body when there isno difference between the multiple frames of the face image and there isalso no difference between the multiple frames of the eye image. Ifthere are differences among the multiple frames of the face image oramong the multiple frames of the eye image, it can be determined thatthe target object is a living body.

In step 230, the target object is identified if the target object is aliving body.

In the embodiments of this specification, a face image and an eye imageof a target object are acquired, and the target object is identifiedbased on at least one of an iris feature or an eye-print feature in theeye image. Because the target object is verified using multiple factorsby combining the face image with at least one of the iris feature or theeye-print feature in the eye image, a user can be better identified.Meanwhile, before identification of the target object, it is firstdetermined whether the target object is a living body, andidentification is performed only when the target object is a livingbody, thereby ensuring the validity of the identification, preventingothers from passing identification using a stolen video or a picture.

In the embodiments of this specification, multiple modes may be used foridentification. For example, at least one of a face image, the irisfeature in an eye image, or the eye-print feature in an eye image may beused for identification. In other words, identification can be performedby combining a face image with the iris feature in an eye image, bycombining a face image with the eye-print feature in an eye image, or byusing a face image, the iris feature in an eye image, and the eye-printfeature in an eye image. Identification may also be performed bycombining the iris feature in an eye image with the eye-print feature inan eye image. The following describes examples of various identificationmodes.

First identification mode: identification based on a face image and theiris feature of an eye image.

In one embodiment of this specification, the eye image may include aniris feature, and in this case, the identifying the target object instep 120 or step 230 may include: comparing the acquired face image withface images in a face image library; selecting a first group of faceimages from the face image library, the first group of face images beingface images whose similarities with the acquired face image are greaterthan a first threshold; further comparing the iris feature of theacquired eye image with iris features of specified eye images in an eyeimage library, wherein the specified eye images are associated with theface images in the first group of face images; and identifying thetarget object successfully if the similarity between an iris feature ofan eye image in the eye image library and the iris feature of theacquired eye image is greater than a second threshold.

This identification mode based on a face image and the iris feature ofan eye image can ensure highly precise identification and has a highidentification speed.

Second identification mode: identification based on a face image and theeye-print feature of an eye image.

In one embodiment of this specification, the eye image comprises aneye-print feature, and the identifying the target object in step 120 orstep 230 may include: comparing the acquired face image with face imagesin a face image library; selecting a first group of face images from theface image library, the first group of face images being face imageswhose similarities with the acquired face image are greater than a firstthreshold; further comparing the eye-print feature of the acquired eyeimage with eye-print features of specified eye images in an eye imagelibrary, wherein the specified eye images are associated with the faceimages in the first group of face images; and identifying the targetobject successfully if the similarity between an eye-print feature of aneye image in the eye image library and the eye-print feature of theacquired eye image is greater than a third threshold.

This identification mode based on a face image and the eye-print featureof an eye image can ensure highly precise identification and has a highidentification speed.

Third identification mode: identification based on a face image, theiris feature of an eye image, and the eye-print feature of an eye image.

In one embodiment of this specification, the eye image includes an irisfeature and an eye-print feature, and the identifying the target objectin step 120 or step 230 includes: comparing the acquired face image withface images in a face image library; selecting a first group of faceimages from the face image library, the first group of face images beingface images whose similarities with the acquired face image are greaterthan a first threshold; further comparing the iris feature of theacquired eye image with iris features of specified eye images in an eyeimage library, wherein the specified eye images are associated with theface images in the first group of face images; selecting a first groupof eye images from the specified eye images, the first group of eyeimages being eye images having iris features whose similarities with theiris feature of the acquired eye image are greater than a secondthreshold in the specified eye images; comparing the eye-print featureof the acquired eye image with eye-print features of the eye images inthe first group of eye images; and identifying the target objectsuccessfully if the similarity between an eye-print feature of an eyeimage in the first group of eye images and the eye-print feature of theacquired eye image is greater than a third threshold.

This identification mode based on a face image, the iris feature of aneye image, and the eye-print feature of an eye image can ensure highlyprecise identification and effectively avoid false identification.

Fourth identification mode: identification based on a face image, theeye-print feature of an eye image, and the iris feature of an eye image.

In one embodiment of this specification, the eye image includes an irisfeature and an eye-print feature, and the identifying the target objectin step 120 or step 230 includes: comparing the acquired face image withface images in a face image library; selecting a first group of faceimages from the face image library, the first group of face images beingface images whose similarities with the acquired face image are greaterthan a first threshold; further comparing the eye-print feature of theacquired eye image with eye-print features of specified eye images in aneye image library, wherein the specified eye images are associated withthe face images in the first group of face images; selecting a secondgroup of eye images from the specified eye images, the second group ofeye images being eye images having eye-print features whose similaritieswith the eye-print feature of the acquired eye image are greater than athird threshold in the specified eye images; comparing the iris featureof the acquired eye image with iris features of the eye images in thesecond group of eye images; and identifying the target objectsuccessfully if the similarity between an iris feature of an eye imagein the second group of eye images and the iris feature of the acquiredeye image is greater than a second threshold.

This identification mode based on a face image, the eye-print feature ofan eye image, and the iris feature of an eye image can ensure highlyprecise identification and effectively avoid false identification.

Fifth identification mode: identification based on the iris feature ofan eye image and a face image.

In one embodiment of this specification, the eye image includes an irisfeature, and the identifying the target object in step 120 or step 230includes: comparing the iris feature of the acquired eye image with irisfeatures of eye images in an eye image library; selecting a third groupof eye images from the eye image library, the third group of eye imagesbeing eye images having iris features whose similarities with the irisfeature of the acquired eye image are greater than a second threshold;comparing the acquired face image with specified face images in a faceimage library, wherein the specified face images are associated with theeye images in the third group of eye images; and identifying the targetobject successfully if the similarity between a face image in thespecified face images and the acquired face image is greater than afirst threshold.

This identification mode based on the iris feature of an eye image and aface image has high precision.

Sixth identification mode: identification based on the eye-print featureof an eye image and a face image.

In one embodiment of this specification, the eye image includes aneye-print feature, and the identifying the target object in step 120 orstep 230 includes: comparing the eye-print feature of the acquired eyeimage with eye-print features of eye images in an eye image library;selecting a fourth group of eye images from the eye image library, thefourth group of eye images being eye images having eye-print featureswhose similarities with the eye-print feature of the acquired eye imageare greater than a third threshold; comparing the acquired face imagewith specified face images in a face image library, wherein thespecified face images are associated with the eye images in the fourthgroup of eye images; and identifying the target object successfully ifthe similarity between a face image in the specified face images and theacquired face image is greater than a first threshold.

This identification mode based on the eye-print feature of an eye imageand a face image has high precision.

Seventh identification mode: identification based on the iris feature ofan eye image, the eye-print feature of an eye image, and a face image.

In one embodiment of this specification, the eye image includes an irisfeature and an eye-print feature, and the identifying the target objectin step 120 or step 230 includes: comparing the iris feature of theacquired eye image with iris features of eye images in an eye imagelibrary; selecting a third group of eye images from the eye imagelibrary, the third group of eye images being eye images having irisfeatures whose similarities with the iris feature of the acquired eyeimage are greater than a second threshold; comparing the eye-printfeature of the acquired eye image with eye-print features of the eyeimages in the third group of eye images; selecting a fifth group of eyeimages from the third group of eye images, the fifth group of eye imagesbeing eye images having eye-print features whose similarities with theeye-print feature of the acquired eye image are greater than a thirdthreshold; comparing the acquired face image with specified face imagesin a face image library, wherein the specified face images areassociated with the fifth group of eye images; and identifying thetarget object successfully if the similarity between a face image in thespecified face images and the acquired face image is greater than afirst threshold.

This identification mode based on the iris feature of an eye image, theeye-print feature of an eye image, and a face image can ensure highlyprecise identification and effectively avoid false identification.

Eighth identification mode: identification based on the eye-printfeature of an eye image, the iris feature of an eye image, and a faceimage.

In one embodiment of this specification, the eye image includes an irisfeature and an eye-print feature, and the identifying the target objectin step 120 or step 230 includes: comparing the eye-print feature of theacquired eye image with eye-print features of eye images in an eye imagelibrary; selecting a fourth group of eye images from the eye imagelibrary, the fourth group of eye images being face images havingeye-print features whose similarities with the eye-print feature of theacquired eye image are greater than a third threshold; comparing theiris feature of the acquired eye image with iris features of the eyeimages in the fourth group of eye images; selecting a sixth group of eyeimages from the fourth group of eye images, the sixth group of eyeimages being eye images having iris features whose similarities with theiris feature of the acquired eye image are greater than a secondthreshold; comparing the acquired face image with specified face imagesin a face image library, wherein the specified face images areassociated with the sixth group of eye images; and identifying thetarget object successfully if the similarity between a face image in thespecified face images and the acquired face image is greater than afirst threshold.

This identification mode based on the eye-print feature of an eye image,the iris feature of an eye image, and a face image can ensure highlyprecise identification and effectively avoid false identification.

It should be noted that in the various identification modes above, theeye image library and the face image library can be obtained locally orfrom other storage apparatuses. Eye images in the eye image library maybe obtained in advance in various manners, and face images in the faceimage library may also be obtained in advance in various manners.Specific acquisition manners are not limited in this application.

Meanwhile, it should be noted that in the various identification modesabove, the first threshold, the second threshold, and the thirdthreshold may be set as required. The first threshold may be set to 70points (100 points in total) or 70%, for example; the first thresholdmay be set to 72 points or 72%, for example; and the third threshold maybe set to 75 points or 75% for example. There is no association amongthe values of the first threshold, the second threshold, and the thirdthreshold.

The identification method in the embodiments can combine face,eye-print, and iris technologies, and the technologies can be applied tonon-cooperative identification of users in a face-scanning paymentscenario, thereby not only combining face-based and eye-print-basedliveness detection technologies, but also using faces, irises, andeye-prints in large-scale user retrieval. Moreover, retrieval can beimplemented in a cascaded manner, thus balancing efficiency andaccuracy, and achieving rapid retrieval from a database includingmillions of faces within a short period of time (such as 1 second).

FIG. 3 is a block diagram of an identification apparatus 300 accordingto an embodiment Referring to FIG. 3, the identification apparatus 300may include an acquisition module 310 and an identification module 320.Here:

The acquisition module 310 is configured to acquire a face image and aneye image of a target object through at least one camera, wherein theeye image includes at least one of an iris feature or an eye-printfeature.

The identification module 320 is configured to identify the targetobject based on the face image and the eye image.

In one embodiment, the identification module 320 is configured to:determine whether the target object is a living body based on the eyeimage and the face image; and identify the target object if the targetobject is a living body.

In one embodiment, the acquisition module 310 is configured to: adjustthe at least one camera to acquire the face image of the target object;and adjust the at least one camera based on the position of a facialrectangular frame of the face image to acquire the eye image of thetarget object.

In one embodiment, the at least one camera is one camera, and theacquisition module 310 is configured to: adjust the camera to obtain theposition of the facial rectangular frame of the target object; acquirethe face image of the target object based on the position of the facialrectangular frame; adjust the camera based on the position of the facialrectangular frame of the face image to obtain the position of a two-eyerectangular frame of the target object; and acquire the eye image of thetarget object based on the position of the two-eye rectangular frame.

In one embodiment, the at least one camera includes a first camera forphotographing a face and a second camera for photographing eyes, and theacquisition module 310 is configured to: adjust the first camera toobtain the position of the facial rectangular frame of the targetobject; acquire the face image of the target object based on theposition of the facial rectangular frame; adjust the second camera basedon the position of the facial rectangular frame to obtain the positionof a two-eye rectangular frame of the target object; and acquire the eyeimage of the target object based on the position of the two-eyerectangular frame.

In one embodiment, the acquisition module 310 is configured to: adjustthe at least one camera to obtain the position of an initial facialrectangular frame of the target object; and adjust the at least onecamera based on the position of the initial facial rectangular frame toacquire the face image and the eye image of the target object.

In one embodiment, the at least one camera is one camera, and theacquisition module 310 is configured to: adjust the camera based on theposition of the initial facial rectangular frame to obtain the positionof a target facial rectangular frame of the target object and theposition of a two-eye rectangular frame of the target object; andacquire the face image of the target object based on the position of thetarget facial rectangular frame, and acquire the eye image of the targetobject based on the position of the two-eye rectangular frame.

In one embodiment, the at least one camera includes a first camera forphotographing a face and a second camera for photographing eyes, and theacquisition module 310 is configured to: adjust the first camera toobtain the position of the initial facial rectangular frame of thetarget object; adjust the first camera based on the position of theinitial facial rectangular frame to obtain the position of a targetfacial rectangular frame of the target object, and acquire the faceimage of the target object based on the position of the target facialrectangular frame; adjust the second camera based on the position of thetarget facial rectangular frame to obtain the position of a two-eyerectangular frame of the target object; and acquire the eye image of thetarget object based on the position of the two-eye rectangular frame.

In one embodiment, the face image is a single-frame face image, the eyeimage is a single-frame eye image, and the identification module 320 isconfigured to: determine whether the target object is a living bodybased on a difference between a single-frame face image of a living bodyand a single-frame face image of a non-living body, and a differencebetween at least one of the iris feature or the eye-print featureincluded in a single-frame eye image of a living body and the at leastone of the iris feature or the eye-print feature included in asingle-frame eye image of a non-living body.

In one embodiment, the face image is a multi-frame face image, the eyeimage is a multi-frame eye image, and the identification module 320 isconfigured to: determine that the target object is a living body ifthere are differences among the multiple frames of the face image;determine that the target object is a living body if there aredifferences among the multiple frames of the eye image in terms of theiris feature or the eye-print feature; and determine that the targetobject is a non-living body if there is no difference between themultiple frames of the eye image in terms of the iris feature or theeye-print feature.

In one embodiment, the eye image includes an iris feature, and theidentification module 320 is configured to: compare the acquired faceimage with face images in a face image library: select a first group offace images from the face image library, the first group of face imagesbeing face images whose similarities with the acquired face image aregreater than a first threshold; further comparing the iris feature ofthe acquired eye image with iris features of specified eye images in aneye image library, wherein the specified eye images are associated withthe face images in the first group of face images; and identify thetarget object successfully if the similarity between an iris feature ofan eye image in the eye image library and the iris feature of theacquired eye image is greater than a second threshold.

In one embodiment, the eye image includes an eye-print feature, and theidentification module 320 is configured to: compare the acquired faceimage with face images in a face image library; select a first group offace images from the face image library, the first group of face imagesbeing face images whose similarities with the acquired face image aregreater than a first threshold; further compare the eye-print feature ofthe acquired eye image with eye-print features of specified eye imagesin an eye image library, wherein the specified eye images are associatedwith the face images in the first group of face images; and identify thetarget object successfully if the similarity between an eye-printfeature of an eye image in the eye image library and the eye-printfeature of the acquired eye image is greater than a third threshold.

In one embodiment, the eye image includes an iris feature and aneye-print feature, and the identification module 320 is configured to:compare the acquired face image with face images in a face imagelibrary; select a first group of face images from the face imagelibrary, the first group of face images being face images whosesimilarities with the acquired face image are greater than a firstthreshold; further compare the iris feature of the acquired eye imagewith iris features of specified eye images in an eye image library,wherein the specified eye images are associated with the face images inthe first group of face images; select a first group of eye images fromthe specified eye images, the first group of eye images being eye imageshaving iris features whose similarities with the iris feature of theacquired eye image are greater than a second threshold in the specifiedeye images; compare the eye-print feature of the acquired eye image witheye-print features of the eye images in the first group of eye images;and identify the target object successfully if the similarity between aneye-print feature of an eye image in the first group of eye images andthe eye-print feature of the acquired eye image is greater than a thirdthreshold.

In one embodiment, the eye image includes an iris feature and aneye-print feature, and the identification module 320 is configured to:compare the acquired face image with face images in a face imagelibrary; select a first group of face images from the face imagelibrary, the first group of face images being face images whosesimilarities with the acquired face image are greater than a firstthreshold; further compare the eye-print feature of the acquired eyeimage with eye-print features of specified eye images in an eye imagelibrary, wherein the specified eye images are associated with the faceimages in the first group of face images; select a second group of eyeimages from the specified eye images, the second group of eye imagesbeing eye images having eye-print features whose similarities with theeye-print feature of the acquired eye image are greater than a thirdthreshold in the specified eye images; compare the iris feature of theacquired eye image with iris features of the eye images in the secondgroup of eye images; and identify the target object successfully if thesimilarity between an iris feature of an eye image in the second groupof eye images and the iris feature of the acquired eye image is greaterthan a second threshold.

In one embodiment, the eye image includes an iris feature, and theidentification module 320 is configured to: compare the iris feature ofthe acquired eye image with iris features of eye images in an eye imagelibrary; select a third group of eye images from the eye image library,the third group of eye images being eye images having iris featureswhose similarities with the iris feature of the acquired eye image aregreater than a second threshold; compare the acquired face image withspecified face images in a face image library, wherein the specifiedface images are associated with the eye images in the third group of eyeimages; and identify the target object successfully if the similaritybetween a face image in the specified face images and the acquired faceimage is greater than a first threshold.

In one embodiment, the eye image includes an eye-print feature, and theidentification module 320 is configured to: compare the eye-printfeature of the acquired eye image with eye-print features of eye imagesin an eye image library; select a fourth group of eye images from theeye image library, the fourth group of eye images being eye imageshaving eye-print features whose similarities with the eye-print featureof the acquired eye image are greater than a third threshold; comparethe acquired face image with specified face images in a face imagelibrary, wherein the specified face images are associated with the eyeimages in the fourth group of eye images; and identify the target objectsuccessfully if the similarity between a face image in the specifiedface images and the acquired face image is greater than a firstthreshold.

In one embodiment, the eye image includes an iris feature and aneye-print feature, and the identification module 320 is configured to:compare the iris feature of the acquired eye image with iris features ofeye images in an eye image library; select a third group of eye imagesfrom the eye image library, the third group of eye images being eyeimages having iris features whose similarities with the iris feature ofthe acquired eye image are greater than a second threshold; compare theeye-print feature of the acquired eye image with eye-print features ofthe eye images in the third group of eye images; select a fifth group ofeye images from the third group of eye images, the fifth group of eyeimages being eye images having eye-print features whose similaritieswith the eye-print feature of the acquired eye image are greater than athird threshold; compare the acquired face image with specified faceimages in the face image library, wherein the specified face images areassociated with the fifth group of eye images; and identify the targetobject successfully if the similarity between a face image in thespecified face images and the acquired face image is greater than afirst threshold.

In one embodiment, the eye image includes an iris feature and aneye-print feature, and the identification module 320 is configured to:compare the eye-print feature of the acquired eye image with eye-printfeatures of eye images in an eye image library; select a fourth group ofeye images from the eye image library, the fourth group of eye imagesbeing face images having eye-print features whose similarities with theeye-print feature of the acquired eye image are greater than a thirdthreshold; compare the iris feature of the acquired eye image with irisfeatures of the eye images in the fourth group of eye images; selectinga sixth group of eye images from the fourth group of eye images, thesixth group of eye images being eye images having iris features whosesimilarities with the iris feature of the acquired eye image are greaterthan a second threshold; compare the acquired face image with specifiedface images in the face image library, wherein the specified face imagesare associated with the sixth group of eye images; and identify thetarget object successfully if the similarity between a face image in thespecified face images and the acquired face image is greater than afirst threshold.

In the embodiments of this specification, a face image and an eye imageof a target object are acquired, and the target object is identifiedbased on at least one of an iris feature or an eye-print feature in theeye image. Because the target object is verified using multiple factorsby combining the face image with at least one of the iris feature or theeye-print feature in the eye image, a user can be better identified.

FIG. 4 is a schematic diagram of a terminal 400 for implementing theabove described methods, according to an embodiment. For example, theterminal 400 may be a mobile terminal.

The terminal 400 includes, but is not limited to, components such as aradio frequency unit 401, a network module 402, an audio output unit403, an input unit 404, a sensor 405, a display 406, a user input 407,an interface 408, a memory 409, a processor 410, and a power supply 411.Those skilled in the art can understand that the structure of theterminal 400 shown in FIG. 4 does not limit the terminal. The terminal400 may include more or fewer components than those shown in FIG. 4, orsome components may be combined, or a different component deployment maybe used. In the embodiments of this specification, the terminal 400includes, but is not limited to, a mobile phone, a tablet computer, anotebook computer, a palmtop computer, a vehicle-mounted terminal, awearable device, a pedometer, and the like.

The processor 410 is configured to generate prompt information, whereinthe prompt information includes a sending location, an amount, andprompt information content; search for another mobile terminal within apreset range of the sending location according to the sending location;and send the prompt information to the another mobile terminal.

In the embodiment, the terminal 400 can generate prompt information,wherein the prompt information includes a sending location, an amount,and prompt information content; search for another mobile terminalwithin a preset range of the sending location according to the sendinglocation; and send the prompt information to the another mobileterminal. The prompt information can be sent anonymously, therebyavoiding the problem of conflicts that occur in face-to-facecommunication and achieving a notification function, improving safety.Moreover, a small amount of money is attached to the prompt information,which can also attract the attention of the audience, and it is morelikely to achieve the information transfer objective.

The radio frequency unit 401 may be configured to send and receiveinformation or place a call. For example, the receiving and sending ofsignals involves receiving downlink data from a base station, and thensending it to the processor 410 for processing. In addition, uplink datais sent to the base station. Generally, the radio frequency unit 401includes, but is not limited to, an antenna, at least one amplifier, atransceiver, a coupler, a low noise amplifier, a duplexer, and the like.In addition, the radio frequency unit 401 may also communicate with anetwork and other devices through a wireless communication system.

The terminal 400 provides wireless broadband Internet access for theuser through the network module 402, for example, helping the user sendand receive emails, browse webpages, access streaming media, and thelike.

The audio output unit 403 can convert audio data, which is received bythe radio frequency unit 401 or the network module 402 or stored in thememory 409, into an audio signal and output the audio signal as sound.Moreover, the audio output unit 403 can further provide an audio output(such as a calling reception sound, a message reception sound, and thelike) related to a specific function performed by the terminal 400. Theaudio output unit 403 includes a loudspeaker, a buzzer, a telephonereceiver, and the like.

The input unit 404 is configured to receive audio or video signals. Theinput unit 404 may include a Graphics Processing Unit (GPU) 4041 and amicrophone 4042. The GPU 4041 processes a static picture or image dataof a video obtained by an image capture apparatus (such as a camera) ina video capture mode or an image capture mode. A processed image framemay be displayed on the display unit 406. Image frames processed by theGPU 4041 may be stored in the memory 409 (or other storage media) orsent through the radio frequency unit 401 or the network module 402. Themicrophone 4042 can receive sound and can process the sound to be audiodata. The processed audio data may be converted, in telephone call mode,into a format that can be sent to a mobile communication base stationthrough the radio frequency unit 401, and output.

The sensor 405 may be, e.g., an optical sensor, a motion sensor, andother sensors. For example, the optical sensor includes an ambient lightsensor and a proximity sensor. The ambient light sensor can adjust theluminance of a display panel 4061 according to brightness of the ambientlight, and the proximity sensor can switch off the display panel 4061and/or backlight when the terminal 400 is moved to the ear. As a type ofmotion sensor, an acceleration sensor can detect the magnitude ofaccelerations in various directions (generally along three axes), candetect the magnitude and direction of gravity when static, and can beused in functions related to recognizing the attitude of the terminal400 (such as switching between landscape mode and portrait mode, relatedgames, and magnetometer attitude calibrations) and vibrationidentification (such as a pedometer and tapping). The sensor 405 mayinclude a fingerprint sensor, a pressure sensor, an iris sensor, amolecule sensor, a gyroscope, a barometer, a hygrometer, a thermometer,an infrared sensor, and the like, which are not described in detailhere.

The display 406 is configured to display information input by the useror provide information for the user. The display 406 may include adisplay panel 4061. The display panel 4061 may be configured in the formof a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode(OLED), or the like.

The user input unit 407 may be configured to receive input digital orcharacter information and generate key signal input related to the usersettings and function control of the terminal 400. For example, the userinput unit 407 includes a touch panel 4071 and other input devices 4072.The touch panel 4071, also referred to as a touch screen, can collect atouch operation by the user on or near the touch panel (such as anoperation by the user on or near the touch panel 4071 using any suitableobject or attachment, such as a finger or a stylus). The touch panel4071 may include two parts: a touch detection apparatus and a touchcontroller. The touch detection apparatus detects the direction of theuser's touch, detects a signal generated by the touch operation, andsends the signal to the touch controller. The touch controller receivestouch information from the touch detection apparatus, converts it intotouch-point coordinates, sends the coordinates to the processor 410, andreceives and executes a command sent by the processor 410. In addition,the touch panel 4071 may be implemented as various types, such as aresistive type, a capacitance type, an infrared type, a surface acousticwave type, and the like. In addition to the touch panel 4071, the userinput unit 407 may further include other input devices 4072. Forexample, the other input devices 4072 may include, but are not limitedto, a physical keyboard, a functional key (such as a volume control key,a switch key, and the like), a track ball, a mouse, and a joystick,which are not described in detail here.

Further, the touch panel 4071 may cover the display panel 4061. Afterdetecting a touch operation thereon or nearby, the touch panel 4071transmits the touch operation to the processor 410 to determine the typeof touch event. Then, the processor 410 provides a corresponding visualoutput on the display panel 4061 according to the type of touch event.Although the touch panel 4071 and the display panel 4061 are implementedas two independent parts in FIG. 4 to achieve the input and outputfunctions of the terminal 400, in some embodiments, the touch panel 4071and the display panel 4061 may be integrated to achieve the input andoutput functions of the terminal 400, which is not specifically limitedhere.

The interface 408 is an interface for connecting an external apparatusto the terminal 400. The external apparatus may be a wired or wirelessheadset port, an external power supply (or battery charger) port, awired or wireless data port, a memory card port, a port for connectionto an apparatus having a recognition module, an audio input/output (I/O)port, a visual I/O port, an earphone port, and the like. The interface408 may be configured to receive input (such as data information,electric power, and the like) from the external apparatus, and transmitthe received input to one or more components in the terminal 400, orconfigured to transmit data between the terminal 400 and the externalapparatus.

The memory 409 may be configured to store software programs and variousdata. The memory 409 may primarily comprise a program storage area and adata storage area. The program storage area can store an operatingsystem, an application program required by at least one function (forexample, a sound playing function, an image display function, and thelike), etc. The data storage area can store data (such as audio data andan address book) created according to use of the mobile phone, and thelike. The memory 409 may include a high-speed random access memory, andmay also include a non-volatile memory, for example, at least onemagnetic disk storage device, a flash storage device, or other volatilesolid-state storage devices.

The processor 410 is the control center of the terminal 400 and connectsvarious parts of the terminal 400 by using various interfaces and lines.By running or executing a software program and/or module stored in thememory 409, and invoking data stored in the memory 409, the processor410 performs various functions of the terminal 400 and processes data,thereby performing overall monitoring of the terminal 400. The processor410 may include one or more processing units. In an embodiment, theprocessor 410 may integrate an application processor and a modemprocessor. The application processor mainly processes operating systems,user interfaces, application programs, and the like. The modem processormainly processes wireless communication. It can be understood that themodem processor may not be integrated into the processor 410.

The terminal 400 may further include a power supply 411 (such as abattery) supplying power to various components. In an embodiment, thepower supply 411 may be logically connected to the processor 410 througha power supply management system, thereby implementing functions such ascharging management, discharging management, and power consumptionmanagement by using the power supply management system.

In addition, the terminal 400 may include some functional modules notshown, which are not described in detail here.

In an embodiment, a mobile terminal includes a processor 410, a memory409, and a computer program that is stored in the memory 409 andoperable on the processor 410. When the computer program is executed bythe processor 410, the mobile terminal performs the above describedidentification method, and the same technical effect can be achieved.

An embodiment of this specification further provides a non-transitorycomputer-readable storage medium has stored thereon instructions that,when executed by a processor of a device, cause the device to performthe above described identification method, and the same technical effectcan be achieved. The computer-readable storage medium is, for example, aRead-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk,an optical disc, or the like.

The identification apparatus and terminal described above can combinethe face, eye-print, and iris technologies, and the technologies can beapplied to identification of non-cooperative users in a face-scanningpayment scenario, thereby not only combining face-based andeye-print-based liveness detection technologies, but also using faces,irises, and eye-prints in large-scale user retrieval. Moreover, theretrieval can be implemented in a cascaded manner, thus balancingefficiency and accuracy, and achieving rapid retrieval from a databaseincluding millions of faces within a short period of time (such as 1second).

It should be noted that, the terms “include,” “comprise” or any othervariations thereof are intended to cover a non-exclusive inclusion, sothat a process, method, article or apparatus including a series ofelements not only includes those elements, but also includes otherelements not expressly listed, or further includes elements inherent tothe process, method, article, or apparatus. In the absence of morelimitations, an element defined by “including a/an . . . ” does notexclude the presence of other, similar elements in the process, method,article or apparatus including the element.

Each of the above described modules and units may be implemented assoftware, or hardware, or a combination of software and hardware. Forexample, each of the above described modules and units may beimplemented using a processor executing instructions stored in a memory.Also, for example, each of the above described modules and units may beimplemented with one or more application specific integrated circuits(ASICs), digital signal processors (DSPs), digital signal processingdevices (DSPDs), programmable logic devices (PLDs), field programmablegate arrays (FPGAs), controllers, micro-controllers, microprocessors, orother electronic components, for performing the above described methods.

Specific embodiments of this specification are described above. Otherembodiments fall within the scope of the appended claims. In some cases,the actions or steps described in the claims may be performed in asequence different from the sequence in the embodiment and still achievethe expected result. In addition, the processes depicted in the drawingsdo not necessarily require the specific sequence shown or a consecutivesequence to achieve the expected result. In some implementations,multi-task processing and parallel processing are also possible or maybe advantageous.

Although the specification has been described in conjunction withspecific embodiments, many alternatives, modifications and variationswill be apparent to those skilled in the art. Accordingly, the followingclaims embrace all such alternatives, modifications and variations thatfall within the terms of the claims.

The invention claimed is:
 1. An identification method, comprising:controlling at least one camera to acquire a face image and an eye imageof a target object, wherein the eye image comprises at least one of aniris feature or an eye-print feature; performing a liveness detection onthe target object based on the face image and the eye image; andidentifying the target object if the target object is detected to be aliving body, wherein when the face image of the target object is asingle-frame face image and the eye image of the target object is asingle-frame eye image, the performing the liveness detection on thetarget object based on the face image and the eye image comprises:determining whether the target object is a living body based on adifference between a single-frame face image of a living body and asingle-frame face image of a non-living body, and a difference betweenat least one of the iris feature or the eye-print feature included in asingle-frame eye image of a living body and the at least one of the irisfeature or the eye-print feature included in a single-frame eye image ofa non-living body; and wherein when the face image of the target objectincludes multiple frames and the eye image of the target object includesmultiple frames, the performing the liveness detection on the targetobject based on the face image and the eye image comprises: determiningthat the target object is a living body if there are differences amongthe multiple frames of the face image; determining that the targetobject is a living body if there are differences among the multipleframes of the eye image in terms of at least one of the iris feature orthe eye-print feature; and determining that the target object is anon-living body if there is no difference between the multiple frames ofthe eye image in terms of the iris feature and the eye-print feature. 2.The method of claim 1, wherein the controlling at least one camera toacquire a face image and an eye image of a target object comprises:adjusting the at least one camera to acquire the face image of thetarget object; and adjusting the at least one camera based on a positionof a facial rectangular frame of the face image to acquire the eye imageof the target object.
 3. The method of claim 2, wherein the at least onecamera is one camera, the adjusting the at least one camera to acquirethe face image of the target object comprises: adjusting the camera toobtain the position of the facial rectangular frame of the targetobject; and acquiring the face image of the target object based on theposition of the facial rectangular frame; and the adjusting the at leastone camera based on a position of a facial rectangular frame of the faceimage to acquire the eye image of the target object comprises: adjustingthe camera based on the position of the facial rectangular frame of theface image to obtain a position of a two-eye rectangular frame of thetarget object; and acquiring the eye image of the target object based onthe position of the two-eye rectangular frame.
 4. The method of claim 2,wherein the at least one camera comprises a first camera forphotographing a face and a second camera for photographing eyes, theadjusting the at least one camera to acquire the face image of thetarget object comprises: adjusting the first camera to obtain theposition of the facial rectangular frame of the target object; andacquiring the face image of the target object based on the position ofthe facial rectangular frame; and the adjusting the at least one camerabased on the position of a facial rectangular frame of the face image toacquire the eye image of the target object comprises: adjusting thesecond camera based on the position of the facial rectangular frame toobtain a position of a two-eye rectangular frame of the target object;and acquiring the eye image of the target object based on the positionof the two-eye rectangular frame.
 5. The method of claim 1, wherein thecontrolling at least one camera to acquire a face image and an eye imageof a target object comprises: adjusting the at least one camera toobtain a position of an initial facial rectangular frame of the targetobject; and adjusting the at least one camera based on the position ofthe initial facial rectangular frame to acquire the face image and theeye image of the target object.
 6. The method of claim 5, wherein the atleast one camera is one camera, and the adjusting the at least onecamera based on the position of the initial facial rectangular frame toacquire the face image and the eye image of the target object comprises:adjusting the camera based on the position of the initial facialrectangular frame to obtain a position of a target facial rectangularframe of the target object and a position of a two-eye rectangular frameof the target object; and acquiring the face image of the target objectbased on the position of the target facial rectangular frame, andacquiring the eye image of the target object based on the position ofthe two-eye rectangular frame.
 7. The method of claim 5, wherein the atleast one camera comprises a first camera for photographing a face and asecond camera for photographing eyes, the adjusting the at least onecamera to obtain a position of an initial facial rectangular frame ofthe target object comprises: adjusting the first camera to obtain theposition of the initial facial rectangular frame of the target object;and the adjusting the at least one camera based on the position of theinitial facial rectangular frame to acquire the face image and the eyeimage of the target object comprises: adjusting the first camera basedon the position of the initial facial rectangular frame to obtain aposition of a target facial rectangular frame of the target object, andacquiring the face image of the target object based on the position ofthe target facial rectangular frame; adjusting the second camera basedon the position of the target facial rectangular frame to obtain aposition of a two-eye rectangular frame of the target object; andacquiring the eye image of the target object based on the position ofthe two-eye rectangular frame.
 8. The method of claim 1, wherein the eyeimage comprises an iris feature, and the identifying the target objectcomprises: comparing the acquired face image with face images in a faceimage library; selecting a first group of face images from the faceimage library, the first group of face images being face images whosesimilarities with the acquired face image are greater than a firstthreshold; further comparing the iris feature of the acquired eye imagewith iris features of specified eye images in an eye image library,wherein the specified eye images are associated with the face images inthe first group of face images; and identifying the target objectsuccessfully if a similarity between an iris feature of an eye image inthe eye image library and the iris feature of the acquired eye image isgreater than a second threshold.
 9. The method of claim 1, wherein theeye image comprises an eye-print feature, and the identifying the targetobject comprises: comparing the acquired face image with face images ina face image library; selecting a first group of face images from theface image library, the first group of face images being face imageswhose similarities with the acquired face image are greater than a firstthreshold; further comparing the eye-print feature of the acquired eyeimage with eye-print features of specified eye images in an eye imagelibrary, wherein the specified eye images are associated with the faceimages in the first group of face images; and identifying the targetobject successfully if a similarity between an eye-print feature of aneye image in the eye image library and the eye-print feature of theacquired eye image is greater than a third threshold.
 10. The method ofclaim 1, wherein the eye image comprises an iris feature and aneye-print feature, and the identifying the target object comprises:comparing the acquired face image with face images in a face imagelibrary; selecting a first group of face images from the face imagelibrary, the first group of face images being face images whosesimilarities with the acquired face image are greater than a firstthreshold; further comparing the iris feature of the acquired eye imagewith iris features of specified eye images in an eye image library,wherein the specified eye images are associated with the face images inthe first group of face images; selecting a first group of eye imagesfrom the specified eye images, the first group of eye images being eyeimages having iris features whose similarities with the iris feature ofthe acquired eye image are greater than a second threshold in thespecified eye images; comparing the eye-print feature of the acquiredeye image with eye-print features of the eye images in the first groupof eye images; and identifying the target object successfully if asimilarity between an eye-print feature of an eye image in the firstgroup of eye images and the eye-print feature of the acquired eye imageis greater than a third threshold.
 11. The method of claim 1, whereinthe eye image comprises an iris feature and an eye-print feature, andthe identifying the target object comprises: comparing the acquired faceimage with face images in a face image library; selecting a first groupof face images from the face image library, the first group of faceimages being face images whose similarities with the acquired face imageare greater than a first threshold; further comparing the eye-printfeature of the acquired eye image with eye-print features of specifiedeye images in an eye image library, wherein the specified eye images areassociated with the face images in the first group of face images;selecting a second group of eye images from the specified eye images,the second group of eye images being eye images having eye-printfeatures whose similarities with the eye-print feature of the acquiredeye image are greater than a third threshold in the specified eyeimages; comparing the iris feature of the acquired eye image with irisfeatures of the eye images in the second group of eye images; andidentifying the target object successfully if a similarity between aniris feature of an eye image in the second group of eye images and theiris feature of the acquired eye image is greater than a secondthreshold.
 12. The method of claim 1, wherein the eye image comprises aniris feature, and the identifying the target object comprises: comparingthe iris feature of the acquired eye image with iris features of eyeimages in an eye image library; selecting a third group of eye imagesfrom the eye image library, the third group of eye images being eyeimages having iris features whose similarities with the iris feature ofthe acquired eye image are greater than a second threshold; comparingthe acquired face image with specified face images in a face imagelibrary, wherein the specified face images are associated with the eyeimages in the third group of eye images; and identifying the targetobject successfully if a similarity between a face image in thespecified face images and the acquired face image is greater than afirst threshold.
 13. The method of claim 1, wherein the eye imagecomprises an eye-print feature, and the identifying the target objectcomprises: comparing the eye-print feature of the acquired eye imagewith eye-print features of eye images in an eye image library; selectinga fourth group of eye images from the eye image library, the fourthgroup of eye images being eye images having eye-print features whosesimilarities with the eye-print feature of the acquired eye image aregreater than a third threshold; comparing the acquired face image withspecified face images in a face image library, wherein the specifiedface images are associated with the eye images in the fourth group ofeye images; and identifying the target object successfully if asimilarity between a face image in the specified face images and theacquired face image is greater than a first threshold.
 14. The method ofclaim 1, wherein the eye image comprises an iris feature and aneye-print feature, and the identifying the target object comprises:comparing the iris feature of the acquired eye image with iris featuresof eye images in an eye image library; selecting a third group of eyeimages from the eye image library, the third group of eye images beingeye images having iris features whose similarities with the iris featureof the acquired eye image are greater than a second threshold; comparingthe eye-print feature of the acquired eye image with eye-print featuresof the eye images in the third group of eye images; selecting a fifthgroup of eye images from the third group of eye images, the fifth groupof eye images being eye images having eye-print features whosesimilarities with the eye-print feature of the acquired eye image aregreater than a third threshold; comparing the acquired face image withspecified face images in a face image library, wherein the specifiedface images are associated with the fifth group of eye images; andidentifying the target object successfully if a similarity between aface image in the specified face images and the acquired face image isgreater than a first threshold.
 15. The method of claim 1, wherein theeye image comprises an iris feature and an eye-print feature, and theidentifying the target object comprises: comparing the eye-print featureof the acquired eye image with eye-print features of eye images in aneye image library; selecting a fourth group of eye images from the eyeimage library, the fourth group of eye images being face images havingeye-print features whose similarities with the eye-print feature of theacquired eye image are greater than a third threshold; comparing theiris feature of the acquired eye image with iris features of the eyeimages in the fourth group of eye images; selecting a sixth group of eyeimages from the fourth group of eye images, the sixth group of eyeimages being eye images having iris features whose similarities with theiris feature of the acquired eye image are greater than a secondthreshold; comparing the acquired face image with specified face imagesin a face image library, wherein the specified face images areassociated with the sixth group of eye images; and identifying thetarget object successfully if a similarity between a face image in thespecified face images and the acquired face image is greater than afirst threshold.
 16. An identification device, comprising: a processor;and a memory configured to store instructions executable by theprocessor, wherein the processor is configured to: control at least onecamera to acquire a face image and an eye image of a target object,wherein the eye image comprises at least one of an iris feature or aneye-print feature; perform a liveness detection on the target objectbased on the face image and the eye image; and identify the targetobject if the target object is detected to be a living body, whereinwhen the face image of the target object is a single-frame face imageand the eye image of the target object is a single-frame eye image,performing the liveness detection on the target object based on the faceimage and the eye image comprises: determining whether the target objectis a living body based on a difference between a single-frame face imageof a living body and a single-frame face image of a non-living body, anda difference between at least one of the iris feature or the eye-printfeature included in a single-frame eye image of a living body and the atleast one of the iris feature or the eye-print feature included in asingle-frame eye image of a non-living body; and wherein when the faceimage of the target object includes multiple frames and the eye image ofthe target object includes multiple frames, performing the livenessdetection on the target object based on the face image and the eye imagecomprises: determining that the target object is a living body if thereare differences among the multiple frames of the face image; determiningthat the target object is a living body if there are differences amongthe multiple frames of the eye image in terms of at least one of theiris feature or the eye-print feature; and determining that the targetobject is a non-living body if there is no difference between themultiple frames of the eye image in terms of the iris feature and theeye-print feature.
 17. A non-transitory computer-readable storage mediumhaving stored thereon instructions that, when executed by a processor ofa device, cause the device to perform an identification method, theidentification method comprising: controlling at least one camera toacquire a face image and an eye image of a target object, wherein theeye image comprises at least one of an iris feature or an eye-printfeature; performing a liveness detection on the target object based onthe face image and the eye image; and identifying the target object ifthe target object is detected to be a living body, wherein when the faceimage of the target object is a single-frame face image and the eyeimage of the target object is a single-frame eye image, the performingthe liveness detection on the target object based on the face image andthe eye image comprises: determining whether the target object is aliving body based on a difference between a single-frame face image of aliving body and a single-frame face image of a non-living body, and adifference between at least one of the iris feature or the eye-printfeature included in a single-frame eye image of a living body and the atleast one of the iris feature or the eye-print feature included in asingle-frame eye image of a non-living body; and wherein when the faceimage of the target object includes multiple frames and the eye image ofthe target object includes multiple frames, the performing the livenessdetection on the target object based on the face image and the eye imagecomprises: determining that the target object is a living body if thereare differences among the multiple frames of the face image; determiningthat the target object is a living body if there are differences amongthe multiple frames of the eye image in terms of at least one of theiris feature or the eye-print feature; and determining that the targetobject is a non-living body if there is no difference between themultiple frames of the eye image in terms of the iris feature and theeye-print feature.